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RIS citation export for MOPSA23: Machine Learning for the Storage Ring Optimization

TY  - CONF
AU  - Fomin, Ye.
ED  - Kuzin, Maksim V.
ED  - Schaa, Volker RW
TI  - Machine Learning for the Storage Ring Optimization
J2  - Proc. of RuPAC2021, Alushta, Crimea, 27 September-01 October 2021
CY  - Alushta, Crimea
T2  - Russian Particle Accelerator Conference
T3  - 27
LA  - english
AB  - The design and optimization of new lattice for modern synchrotron radiation sources are for the most part an art and highly dependent on the researcher skills. Since both modern existing and designing storage rings is very complex nonlinear system the researchers spend a lot of effort to solve their problems. In this work the use of machine learning technics to improve the efficiency of solving nonlinear systems optimization problems is considered.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 169
EP  - 171
KW  - synchrotron
KW  - ion-source
KW  - radiation
KW  - synchrotron-radiation
KW  - emittance
DA  - 2021/10
PY  - 2021
SN  - 2673-5539
SN  - 978-3-95450-240-0
DO  - doi:10.18429/JACoW-RuPAC2021-MOPSA23
UR  - https://jacow.org/rupac2021/papers/mopsa23.pdf
ER  -